Atin Ghosh

Enterprise AI agents can now generate valid, high-fidelity tabular data for training and evaluation without needing access to sensitive schemas or real systems, bypassing critical industry bottlenecks.

EdgeGen is a synthetic task generation framework that extracts compliance rules from an agent's specification and uses them to generate database-grounded edge-case tasks designed to violate these rules, and when combined with existing synthetic data generation techniques, enables agent improvement through finetuning and harness optimization.

Model rankings on standard benchmarks can flip entirely when you optimize prompts for each LLM, so your "best" model might actually be the worst.